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Adversarial machine learning is concerned with the design of ML algorithms that can resist security challenges, the study of the capabilities of attackers, and the understanding of attack consequences.
Adversarial Machine Learning states that there are four types of attacks that ML models can suffer.
There’s no denying that consumers want credible, relevant, current, and easy-to-understand information in order to make the right purchase decisions that reflect their particular preferences. In the age of information overload, probably what we need most right now is better consumer information. Price comparison websites, for example, have made a huge difference in making it much easier for customers to evaluate different offers and make the best purchasing decision.
In recent years, the consumer credit industry has seen an increase in the number of new suppliers and types of consumer credit. Not knowing how online platforms work and the new types of credit arrangements can swiftly put consumers at a significant disadvantage.
Software security is a critical topic that has been the focus of attention of many researchers and professionals over the years. One of the reasons this subject does not lose relevance is the number of vulnerabilities that become known each day. According to NVD (2006), a vulnerability can be defined as "a weakness in the computational logic (e.g., code) found in software and hardware components that, when exploited, results in a negative impact on confidentiality, integrity, or availability". The numbers surrounding this type of computational issue give a more concrete panorama about its criticality. Seacord (2013) states that in a period of nine years — from 2004 to 2012 — a total of 45,135 vulnerabilities were reported and cataloged by the National Vulnerabilities Database — NVD — of the National Institute of Standards and Technology — NIST. Yet, only in the year 2019, the NVD received 18,938 new entries.
In terms of secure programming, two categories of vulnerabilities have been exploited by attackers so that to damage the correct running flow of a software. The first one is String-related vulnerabilities. Basically, failures of this category cause a kind of security threat known as buffer overflow. Another widespread category of vulnerabilities is Integer-related. The most common errors found out in this category are overflow and signed errors. All those situations can lead to unexpected behaviors and the system can end up in an unsecured state. In scenarios like those ones, mitigation strategies turned to avoid jeopardizing a program or even a whole system generally are based on a solid knowledge of the employed programming language and in an accurate manner to explore its resources.
The recent surge in Work-From-Home, triggered by the COVID-19 crisis, is here to stay and the first sign of it is that "WFH" has been added to the alphabet soup of jargons crowding the technology industry. WFH, however, has also created a fresh set of challenges for organizations to protect their intellectual assets from cyberattacks. It’s a no-brainer to say that our home networks are far more vulnerable than enterprise networks. Companies are leveraging this crisis to meet immediate needs as well as for building more lasting, longer-term access to a variety of resources in the cloud as well as in the enterprise data center.
As the world logs on to enterprise networks from home, the demand for more secure remote access for employees is at an all-time high. Organizations must prepare for possible cyberattacks on our home IT networks to exploit its vulnerabilities. They need to monitor IT use for signs of malicious behaviour, safeguard sensitive data and assure maximum compliance with privacy and regulatory requirements. Also, the extensive use of cloud services necessitated by the COVID-19 crisis, both on-premise and public, will compel enterprises to reassess this ecosystem and take additional steps to protect it.
In CloudHub, for application deployment, we normally have requirements to hide or encrypt properties. It completely depends on your preference. CloudHub resolves the properties at runtime without exposing sensitive information.
Encryption of Mule application properties is another feature and one of the easiest ways to secure sensitive information.
Secure hash functions are practically impossible to reverse, but only if the input is unrestricted.
If you generate 256 random bits and apply a secure 256-bit hash algorithm, an attacker wanting to recover your input can’t do much better than brute force hashing 256-bit strings hoping to find one that matches your hash value. Even then, the attacker may find a collision, another string of bits that happens to have the same hash value.